Face images in the wild will undergo large intra-personal variations, such as in poses, illuminations, occlusions and resolutions. Dealing with variations of face images is the key challenge in many face-related applications.
To deal with face variation, there are methods for face normalization in which an image in a canonical view frontal pose and neutral lighting) is recovered from a face image under a large pose and a different lighting. The face normalization methods can be generally separated into two categories: 3D- and 2D-based face reconstruction methods. The 3D-based methods aim to recover the frontal pose by 3D geometrical transformations. The 2D-based methods infer the frontal pose with graphical models, such as Markov Random Fields (MU), where the correspondences are learned from images in different poses. The above methods have certain limitations, such as capturing 3D data adds additional cost and resources, and 2D face synthesis depends heavily on good alignment, while the results are often not smooth on real-world images. Furthermore, these methods were mostly evaluated on face images collected under controlled conditions, either in employed 3D information or in controlled 2D environment.
Therefore, to address at least one or more of the above problems, it is desirable to provide a system and a method for verifying face images based on canonical images in which the canonical image for each identity can be automatically selected or synthesized so that the intra-person variances are reduced, while the inter-person discriminative capabilities are maintained.